Defining the regulatory architecture of hematopoietic cells to elucidate lineage determination and differentiation can produce insights into developmental biology and can help identify targets with potential application to human diseases such as leukemias and anemias. Mouse hematopoiesis is a versatile system for studying gene regulation during differentiation because we can purify populations of progenitor and differentiated cells for genome-wide mapping of transcripts and regulatory sequences, and we can genetically manipulate critical proteins and cis-regulatory modules (CRMs) to study mechanisms of regulation. This application is for a renewal of a long-standing, productive collaboration among multiple investigators with complementary expertise in hematopoietic cell differentiation, gene regulation, genomics, bioinformatics and statistics. Our previous work laid a foundation of genome-wide data sets for transcriptomes, transcription factor occupancy and chromatin states in a cultured cell model for erythroid differentiation and in maturing primary cells in the erythroid and megakaryocytic lineages, which led to key new insights about regulation. We now propose to (Aim 1) generate genome-wide data on transcriptomes and informative epigenetic features in purified cells from each stage of differentiation from mouse hematopoietic stem cells to mature cells of the erythroid and myeloid lineages. For all cell types, including multilineage progenitor cells available only in small numbers, we propose to determine transcriptomes, DNA methylation, and chromatin accessibility (using a new method based on in vitro transposition). In more abundant cell types, we will use ChIP-seq to map transcription factors and histone modifications and also the chromosome conformation capture method Hi-C to build an interaction map of distal regulatory regions with target genes. We will then (Aim 2) conduct integrative, quantitative modeling to find genes differentially expressed and with different transcription factor binding patterns in the distinct lineages; within this set are candidates for genes involved in choice of cell lineage. A hypothesis-driven Bayesian network model will learn quantitative relationships between features, including expression level, and make predictions about how the system would behave after perturbation of both transcription factors and CRMs. We will then (Aim 3) conduct genetic manipulations to test hypotheses arising from integrative analysis in Aim 2. Specific hypotheses about genes involved in lineage choice will be tested by transduction of interfering or forced expression constructs into mouse fetal liver progenitor cells and bipotential cells in culture. Hypotheses from the quantitative modeling of determinants of levels of expression will be tested, targeting specific proteins (using transfections of cells with or withou GATA1) and CRMs (by Cas9-CRISPR-guided genome editing). The result of this proposed work will be deep, widely disseminated data on the regulatory landscape in multiple hematopoietic lineages and keener insights into how changes in regulatory proteins and chromatin lead to lineage choice and progressive differentiation.